Dynamic lot-sizing model with demand time windows and speculative cost structure

We consider a deterministic lot-sizing problem with demand time windows, where speculative motive is allowed. Utilizing an untraditional decomposition principle, we provide an optimal algorithm that runs in O(nT³) time, where n is the number of demands and T is the length of the planning horizon.     

An Efficient Procedure for Dynamic Lot-sizing Model with Demand Time Windows

We consider a dynamic lot-sizing model with demand time windows where n demands need to be scheduled in T production periods. For the case of backlogging allowed, an O(T ³) algorithm exists under the non-speculative cost structure. For the same model with somewhat general cost structure, we propose an efficient algorithm with O(max {T ², nT}) time complexity.     

An efficient approach for solving the lot-sizing problem with time-varying storage capacities

We address the dynamic lot size problem assuming time-varying storage capacities. The planning horizon is divided into T periods and stockouts are not allowed. Moreover, for each period, we consider a setup cost, a holding unit cost and a production/ordering unit cost, which can vary through the planning horizon. Although this model can be solved using O(T³) algorithms already introduced in the specialized literature, we show that under this cost structure an optimal solution can be obtained in O(T log T) time. In addition, we show that when production/ordering unit costs are assumed to be constant (i.e., the Wagner–Whitin case), there exists an optimal plan satisfying the Zero Inventory Ordering (ZIO) property.     

An O(n2) simplex algorithm for a class of linear programs with tree structure

In connection with the optimal design of centralized circuit-free networks linear 0–1 programming problems arise which are related to rooted trees. For each problem the variables correspond to the edges of a given rooted tree T. Each path from a leaf to the root of T, together with edge weights, defines a linear constraint, and a global linear objective is to be maximized. We consider relaxations of such problems where the variables are not restricted to 0 or 1 but are allowed to vary continouosly between these bounds. The values of the optimal solutions of such relaxations may be used in a branch and bound procedure for the original 0–1 problem. While in [10] a primal algorithm for these relaxations is discussed, in this paper, we deal with the dual linear program and present a version of the simplex algorithm for its solution which can be implemented to run in time O(n²). For balanced trees T this time can be reduced to O(n log n).     

Dynamic lot-sizing model for major and minor demands

This paper deals with a lot-sizing model for major and minor demands in which major demands are specified by time windows while minor demands are given by periods. For major demands, the agreeable time window structure is assumed where each time window is not strictly nested in any other time windows. To incorporate the economy of scale of large production quantity, especially from major demands, concave cost structure in production must be considered. Investigating the optimality properties, we propose optimal solution procedures based on dynamic program. For a simple case when only major demands exist, we propose an optimal procedure with running time of O(n²T)$\mathrm{O}(n^{2}T)$ where n is the number of demands and T   is the length of the planning horizon. Extending the algorithm to the model with major and minor demands, we propose an algorithm with complexity O(n²T²)$\mathrm{O}(n^2{T}^2)$.     

Minimum flow problem on network flows with time-varying bounds

In this paper, we consider the minimum flow problem on network flows in which the lower arc capacities vary with time. We will show that this problem for set {0, 1, … , T} of time points can be solved by at most n minimum flow computations, by combining of preflow-pull algorithm and reoptimization techniques (no matter how many values of T are given). Running time of the presented algorithm is O(n²m).     

The just-in-time scheduling problem in a flow-shop scheduling system

We study the problem of maximizing the weighted number of just-in-time (JIT) jobs in a flow-shop scheduling system under four different scenarios. The first scenario is where the flow-shop includes only two machines and all the jobs have the same gain for being completed JIT. For this scenario, we provide an O(n³) time optimization algorithm which is faster than the best known algorithm in the literature. The second scenario is where the job processing times are machine-independent. For this scenario, the scheduling system is commonly referred to as a proportionate flow-shop. We show that in this case, the problem of maximizing the weighted number of JIT jobs is NP-hard in the ordinary sense for any arbitrary number of machines. Moreover, we provide a fully polynomial time approximation scheme (FPTAS) for its solution and a polynomial time algorithm to solve the special case for which all the jobs have the same gain for being completed JIT. The third scenario is where a set of identical jobs is to be produced for different customers. For this scenario, we provide an O(n³) time optimization algorithm which is independent of the number of machines. We also show that the time complexity can be reduced to O(n log n) if all the jobs have the same gain for being completed JIT. In the last scenario, we study the JIT scheduling problem on m machines with a no-wait restriction and provide an O(mn²) time optimization algorithm.     

Comparative studies on dynamic programming and integer programming approaches for concave cost production/inventory control problems

This paper is concerned with classical concave cost multi-echelon production/inventory control problems studied by W. Zangwill and others. It is well known that the problem with m production steps and n time periods can be solved by a dynamic programming algorithm in O(n ⁴ m) steps, which is considered as the fastest algorithm for solving this class of problems. In this paper, we will show that an alternative 0–1 integer programming approach can solve the same problem much faster particularly when n is large and the number of 0–1 integer variables is relatively few. This class of problems include, among others problem with set-up cost function and piecewise linear cost function with fewer linear pieces. The new approach can solve problems with mixed concave/convex cost functions, which cannot be solved by dynamic programming algorithms.     

Fast solution of toeplitz systems of equations and computation of Padé approximants

We present two new algorithms, ADT and MDT, for solving order-n Toeplitz systems of linear equations Tz = b in time O(n log²n) and space O(n). The fastest algorithms previously known, such as Trench's algorithm, require time $\text{Ω(n}{}^2\text{)}$ and require that all principal submatrices of T be nonsingular. Our algorithm ADT requires only that T be nonsingular. Both our algorithms for Toeplitz systems are derived from algorithms for computing entries in the Padé table for a given power series. We prove that entries in the Padé table can be computed by the Extended Euclidean Algorithm. We describe an algorithm EMGCD (Extended Middle Greatest Common Divisor) which is faster than the algorithm HGCD of Aho, Hopcroft and Ullman, although both require time O(n log²n), and we generalize EMGCD to produce PRSDC (Polynomial Remainder Sequence Divide and Conquer) which produces any iterate in the PRS, not just the middle term, in time O(n log²n). Applying PRSDC to the polynomials U₀(x) = x²ⁿ⁺¹ and U₁(x) = a₀ + a₁x + … + a_2nx²ⁿ gives algorithm AD (Anti-Diagonal), which computes any (m, p) entry along the antidiagonal m + p = 2n of the Padé table for U₁ in time O(n log²n). Our other algorithm, MD (Main-Diagonal), computes any diagonal entry (n, n) in the Padé table for a normal power series, also in time O(n log²n). MD is related to Schönhage's fast continued fraction algorithm. A Toeplitz matrix T is naturally associated with U₁, and the (n, n) Padé approximation to U₁ gives the first column of T^?1. We show how a formula due to Trench can be used to compute the solution z of Tz = b in time O(n log n) from the first row and column of T^?1. Thus, the Padé table algorithms AD and MD give O(n log²n) Toeplitz algorithms ADT and MDT. Trench's formula breaks down in certain degenerate cases, but in such cases a companion formula, the discrete analog of the Christoffel-Darboux formula, is valid and may be used to compute z in time O(n log²n) via the fast computation (by algorithm AD) of at most four Padé approximants. We also apply our results to obtain new complexity bounds for the solution of banded Toeplitz systems and for BCH decoding via Berlekamp's algorithm.     

An efficient parallel algorithm for shifting the root of a depth first spanning tree

Given a simple, connected, undirected graph G on n vertices and a depth first spanning tree (dfst) T of G with root t, the root-shifting problem is to find a dfst R of G with a specified root r. We present an O(log n) step algorithm for solving this problem using n³ processors on a parallel computer which does not permit concurrent writes but allows concurrent reads. We also discuss a processor allocation strategy for this algorithm which can be implemented without increasing the overall running time of the algorithm.     

An Improved Algorithm for the Traveler′s Problem

In a paper in Journal of Algorithms13 (1992), 148-160, Hirschberg and Larmore introduced the traveler′s problem as a subroutine for constructing the B-tree. They gave an O(n^5/3 log^1/3n) time algorithm for solving the traveler′s problem of size n. In this paper, we improve their time bound to O(n^3/2 log n). As a consequence, we build a B-tree in O(n^3/2 log²n) time as compared to the O(n^5/3 log^4/3n) time algorithm of Hirschberg and Larmore.     

A capacity-rounding algorithm for the minimum-cost circulation problem: A dual framework of the Tardos algorithm

Recently, É. Tardos gave a strongly polynomial algorithm for the minimum-cost circulation problem and solved the open problem posed in 1972 by J. Edmonds and R.M. Karp. Her algorithm runs in O(m ² T(m, n) logm) time, wherem is the number of arcs,n is the number of vertices, andT(m, n) is the time required for solving a maximum flow problem in a network withm arcs andn vertices. In the present paper, taking an approach that is a dual of Tardos's, we also give a strongly polynomial algorithm for the minimum-cost circulation problem. Our algorithm runs in O(m ² S(m, n) logm) time and reduces the computational complexity, whereS(m, n) is the time required for solving a shortest path problem with a fixed origin in a network withm arcs,n vertices, and a nonnegative arc length function. The complexity is the same as that of Orlin's algorithm, recently developed by efficiently implementing the Edmonds-Karp scaling algorithm.     

Fully polynomial time approximation scheme for the total weighted tardiness minimization with a common due date

This paper deals with the total weighted tardiness minimization with a common due date on a single machine. The best previous approximation algorithm for this problem was recently presented in [H. Kellerer, V.A. Strusevich, A fully polynomial approximation scheme for the single machine weighted total tardiness problem with a common due date, Theoretical Computer Science 369 (2006) 230-238] by Kellerer and Strusevich. They proposed a fully polynomial time approximation scheme (FPTAS) of O((n⁶logW)/ε³) time complexity (W is the sum of weights, n is the number of jobs and ε is the error bound). For this problem, we propose a new approach to obtain a more effective FPTAS of O(n²/ε) time complexity. Moreover, a more effective and simpler dynamic programming algorithm is designed.     

A polynomial time algorithm for solving a quality control station configuration problem

We study unreliable serial production lines with known failure probabilities for each operation. Such a production line consists of a series of stations; existing machines and optional quality control stations (QCS). Our aim is to simultaneously decide where and if to install the QCSs along the line and to determine the production rate, so as to maximize the steady state expected net profit per time unit from the system.We use dynamic programming to solve the cost minimization auxiliary problem where the aim is to minimize the time unit production cost for a given production rate. Using the above developed O(N²) dynamic programming algorithm as a subroutine, where N stands for the number of machines in the line, we present an O(N⁴) algorithm to solve the Profit Maximization QCS Configuration Problem.     

A 5.875-approximation for the Traveling Tournament Problem

In this paper we propose an approximation for the Traveling Tournament Problem which is the problem of designing a schedule for a sports league consisting of a set of teams T such that the total traveling costs of the teams are minimized. It is not allowed for any team to have more than k home-games or k away-games in a row. We propose an algorithm which approximates the optimal solution by a factor of 2+2k/n+k/(n?1)+3/n+3/(2?k) which is not more than 5.875 for any choice of k≥4 and n≥6. This is the first constant factor approximation for k>3. We furthermore show that this algorithm is also applicable to real-world problems as it produces solutions of high quality in a very short amount of time. It was able to find solutions for a number of well known benchmark instances which are even better than the previously known ones.     

Inviscid limit for the energy-critical complex Ginzburg-Landau equation

In this paper, we consider the limit behavior for the solution of the Cauchy problem of the energy-critical complex Ginzburg-Landau equation in Rn, n?3. In lower dimension case (3?n?6), we show that its solution converges to that of the energy-critical nonlinear Schrödinger equation in , T>0, s=0,1, as a by-product, we get the regularity of solutions in H³ for the nonlinear Schrödinger equation. In higher dimension case (n>6), we get the similar convergent behavior in C(0,T,L²(Rn)). In both cases we obtain the optimal convergent rate.     

A polynomial algorithm to compute the minimum degree spanning trees of directed acyclic graphs with applications to the broadcast problem

In this paper, we focus on the directed minimum degree spanning tree problem and the minimum time broadcast problem. Firstly, we propose a polynomial time algorithm for the minimum degree spanning tree problem in directed acyclic graphs. The algorithm starts with an arbitrary spanning tree, and iteratively reduces the number of vertices of maximum degree. We can prove that the algorithm must reduce a vertex of the maximum degree for each phase, and finally result in an optimal tree. The algorithm terminates in O(mnlogn) time, where m and n are the numbers of edges and vertices of the graph, respectively. Moreover, we apply the new algorithm to the minimum time broadcast problem. Two consequences for directed acyclic graphs are: (1) the problem under the vertex-disjoint paths mode can be approximated within a factor of of the optimum in O(mnlogn)-time; (2) the problem under the edge-disjoint paths mode can be approximated within a factor of O(Δ^*/logΔ^*) of the optimum in O(mnlogn)-time, where Δ^* is the minimum k such that there is a spanning tree of the graph with maximum degree k.     

An exact algorithm for MAX-CUT in sparse graphs

We study exact algorithms for the MAX-CUT problem. Introducing a new technique, we present an algorithmic scheme that computes a maximum cut in graphs with bounded maximum degree. Our algorithm runs in time O^*(2^(1-(2/Δ))n). We also describe a MAX-CUT algorithm for general graphs. Its time complexity is O^*(2^mn/(m+n)). Both algorithms use polynomial space.     

An O(T) algorithm for the capacitated lot sizing problem with minimum order quantities

This paper explores a single-item capacitated lot sizing problem with minimum order quantity, which plays the role of minor set-up cost. We work out the necessary and sufficient solvability conditions and apply the general dynamic programming technique to develop an O(T³) exact algorithm that is based on the concept of minimal sub-problems. An investigation of the properties of the optimal solution structure allows us to construct explicit solutions to the obtained sub-problems and prove their optimality. In this way, we reduce the complexity of the algorithm considerably and confirm its efficiency in an extensive computational study.     

Reoptimizing the 0-1 knapsack problem

In this paper we study the problem where an optimal solution of a knapsack problem on n items is known and a very small number k of new items arrive. The objective is to find an optimal solution of the knapsack problem with n+k items, given an optimal solution on the n items (reoptimization of the knapsack problem). We show that this problem, even in the case k=1, is NP-hard and that, in order to have effective heuristics, it is necessary to consider not only the items included in the previously optimal solution and the new items, but also the discarded items. Then, we design a general algorithm that makes use, for the solution of a subproblem, of an α-approximation algorithm known for the knapsack problem. We prove that this algorithm has a worst-case performance bound of , which is always greater than α, and therefore that this algorithm always outperforms the corresponding α-approximation algorithm applied from scratch on the n+k items. We show that this bound is tight when the classical Ext-Greedy algorithm and the algorithm are used to solve the subproblem. We also show that there exist classes of instances on which the running time of the reoptimization algorithm is smaller than the running time of an equivalent PTAS and FPTAS.